import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets('MNIST_data', one_hot = True)

 #

 # add layer

 #

def add_layer(inputs, in_size, out_size, activation_function = None):  

    Weights = tf.Variable(tf.random_normal([in_size, out_size]))  # hang lie

    biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)

    Wx_plus_b = tf.matmul(inputs, Weights) + biases  

    if activation_function is None:

      outputs = Wx_plus_b

    else:

      outputs = activation_function(Wx_plus_b)  

    return outputs  

def compute_accuracy(v_xs, v_ys):

  global prediction

  y_pre = sess.run(prediction, feed_dict={xs:v_xs})

  correct_prediction = tf.equal(tf.argmax(y_pre, 1), tf.argmax(v_ys, 1))#返回最大值的索引号

  accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

  result = sess.run(accuracy, feed_dict={xs:v_xs, ys:v_ys})

  return result

#

# define placeholder for inputs to network

#

xs = tf.placeholder(tf.float32, [None, 784]) # 28x28, 784 dimention / sample

ys = tf.placeholder(tf.float32, [None, 10])

#

# add output layer

#

prediction = add_layer(xs, 784, 10, activation_function = tf.nn.softmax)

#

# the error between prediction and real data

#

cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction),

					      reduction_indices=[1])) #loss

train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

sess = tf.Session()

sess.run(tf.global_variables_initializer())

for i in range(1000):

  batch_xs, batch_ys = mnist.train.next_batch(100)

  sess.run(train_step, feed_dict={xs:batch_xs, ys:batch_ys})

  if i % 50 == 0:

    print(compute_accuracy(

      mnist.test.images, mnist.test.labels))